Comonotonicity

In probability theory, comonotonicity mainly refers to the perfect positive dependence between the components of a random vector, essentially saying that they can be represented as increasing functions of a single random variable. In two dimensions it is also possible to consider perfect negative dependence, which is called countermonotonicity. Comonotonicity is also related to the comonotonic additivity of the Choquet integral.[1]

The concept of comonotonicity has applications in financial risk management and actuarial science, see e.g. (Dhaene Denuit) and (Dhaene Denuit). In particular, the sum of the components X1 \+ X2 \+ · · · + Xn is the riskiest if the joint probability distribution of the random vector (X1, X2, . . . , Xn) is comonotonic.[2] Furthermore, the α-quantile of the sum equals of the sum of the α-quantiles of its components, hence comonotonic random variables are quantile-additive.[3][4] In practical risk management terms it means that there is minimal (or eventually no) variance reduction from diversification.

For extensions of comonotonicity, see (Jouini Napp) and (Puccetti Scarsini).

## Contents

* 1 Definitions
* 1.1 Comonotonicity of subsets of Rn
* 1.2 Comonotonicity of probability measures on Rn
* 1.3 Comonotonicity of Rn-valued random vectors
* 2 Properties
* 3 Upper bounds
* 3.1 Upper Fréchet–Hoeffding bound for cumulative distribution functions
* 3.2 Upper bound for the covariance
* 4 See also
* 5 Notes
* 6 Citations
* 7 References

## Definitions

### Comonotonicity of subsets of Rn

A subset S of Rn is called comonotonic[5] (sometimes also nondecreasing[6]) if, for all (x1, x2, . . . , xn) and (y1, y2, . . . , yn) in S with xi < yi for some i ∈ {1, 2, . . . , n}, it follows that xj ≤ yj for all j ∈ {1, 2, . . . , n}.

This means that S is a totally ordered set.

### Comonotonicity of probability measures on Rn

Let μ be a probability measure on the n-dimensional Euclidean space Rn and let F denote its multivariate cumulative distribution function, that is

F(x1,…,xn):=μ({(y1,…,yn)∈ℝn∣y1≤x1,…,yn≤xn}),(x1,…,xn)∈ℝn.

Furthermore, let F1, . . . , Fn denote the cumulative distribution functions of the n one-dimensional marginal distributions of μ, that means

Fi(x):=μ({(y1,…,yn)∈ℝn∣yi≤x}),x∈ℝ

for every i ∈ {1, 2, . . . , n}. Then μ is called comonotonic, if

F(x1,…,xn)=mini∈{1,…,n}Fi(xi),(x1,…,xn)∈ℝn.

Note that the probability measure μ is comonotonic if and only if its support S is comonotonic according to the above definition.[7]

### Comonotonicity of Rn-valued random vectors

An Rn-valued random vector X = (X1, . . . , Xn) is called comonotonic, if its multivariate distribution (the pushforward measure) is comonotonic, this means

Pr⁡(X1≤x1,…,Xn≤xn)=mini∈{1,…,n}Pr⁡(Xi≤xi),(x1,…,xn)∈ℝn.

## Properties

An Rn-valued random vector X = (X1, . . . , Xn) is comonotonic if and only if it can be represented as

(X1,…,Xn)=d(FX1−1(U),…,FXn−1(U)),

where =d stands for equality in distribution, on the right-hand side are the left-continuous generalized inverses[8] of the cumulative distribution functions FX1, . . . , FXn, and U is a uniformly distributed random variable on the unit interval. More generally, a random vector is comonotonic if and only if it agrees in distribution with a random vector where all components are non-decreasing functions (or all are non-increasing functions) of the same random variable.[9]

## Upper bounds

### Upper Fréchet–Hoeffding bound for cumulative distribution functions

Let X = (X1, . . . , Xn) be an Rn-valued random vector. Then, for every i ∈ {1, 2, . . . , n},

Pr⁡(X1≤x1,…,Xn≤xn)≤Pr⁡(Xi≤xi),(x1,…,xn)∈ℝn,

hence

Pr⁡(X1≤x1,…,Xn≤xn)≤mini∈{1,…,n}Pr⁡(Xi≤xi),(x1,…,xn)∈ℝn,

with equality everywhere if and only if (X1, . . . , Xn) is comonotonic.

### Upper bound for the covariance

Let (X, Y) be a bivariate random vector such that the expected values of X, Y and the product XY exist. Let (X*, Y*) be a comonotonic bivariate random vector with the same one-dimensional marginal distributions as (X, Y).[note 1] Then it follows from Höffding's formula for the covariance[10] and the upper Fréchet–Hoeffding bound that

Cov(X,Y)≤Cov(X*,Y*)

and, correspondingly,

E⁡[XY]≤E⁡[X*Y*]

with equality if and only if (X, Y) is comonotonic.[11]

Note that this result generalizes the rearrangement inequality and Chebyshev's sum inequality.

## See also

* Copula (probability theory)

## Notes

1. ↑ (X*, Y*) always exists, take for example (FX−1(U), FY −1(U)), see section Properties above.

## Citations

1. ↑ (Sriboonchitta Wong)
2. ↑ (Kaas Dhaene)
3. ↑ (Kaas Dhaene)
4. ↑ (McNeil Frey)
5. ↑ (Kaas Dhaene)
6. ↑ See (Nelsen 2006) for the case n = 2
7. ↑ See (Nelsen 2006) for the case n = 2
8. ↑ (McNeil Frey)
9. ↑ (McNeil Frey)
10. ↑ (McNeil Frey)
11. ↑ (McNeil Frey)

## References

* Dhaene, Jan; Denuit, Michel; Goovaerts, Marc J.; Vyncke, David (2002a), "The concept of comonotonicity in actuarial science and finance: theory", Insurance: Mathematics & Economics 31 (1): 3–33, doi:10.1016/s0167-6687(02)00134-8, http://www.econ.kuleuven.be/insurance/pdfs/DdgkvTh.pdf, retrieved 2012-08-28
* Dhaene, Jan; Denuit, Michel; Goovaerts, Marc J.; Vyncke, David (2002b), "The concept of comonotonicity in actuarial science and finance: applications", Insurance: Mathematics & Economics 31 (2): 133–161, doi:10.1016/s0167-6687(02)00135-x, http://www.econ.kuleuven.be/insurance/pdfs/DDGKVapp.pdf, retrieved 2012-08-28
* Jouini, Elyès; Napp, Clotilde (2004), "Conditional comonotonicity", Decisions in Economics and Finance 27 (2): 153–166, doi:10.1007/s10203-004-0049-y, ISSN 1593-8883, http://www.ceremade.dauphine.fr/~jouini/DEF180RR.pdf
* Kaas, Rob; Dhaene, Jan; Vyncke, David; Goovaerts, Marc J.; Denuit, Michel (2002), "A simple geometric proof that comonotonic risks have the convex-largest sum", ASTIN Bulletin 32 (1): 71–80, doi:10.2143/ast.32.1.1015, http://www.casact.org/library/astin/vol32no1/71.pdf
* McNeil, Alexander J.; Frey, Rüdiger; Embrechts, Paul (2005), Quantitative Risk Management. Concepts, Techniques and Tools, Princeton Series in Finance, Princeton, NJ: Princeton University Press, ISBN 978-0-691-12255-7, https://books.google.com/books?id=f5J_OZPeq50C
* Nelsen, Roger B. (2006), An Introduction to Copulas, Springer Series in Statistics (second ed.), New York: Springer, pp. xiv+269, ISBN 978-0-387-28659-4
* Puccetti, Giovanni; Scarsini, Marco (2010), "Multivariate comonotonicity", Journal of Multivariate Analysis 101 (1): 291–304, doi:10.1016/j.jmva.2009.08.003, ISSN 0047-259X, http://www.parisschoolofeconomics.eu/IMG/pdf/MED090320-Scarsini.pdf
* Sriboonchitta, Songsak; Wong, Wing-Keung; Dhompongsa, Sompong; Nguyen, Hung T. (2010), Stochastic Dominance and Applications to Finance, Risk and Economics, Boca Raton, FL: Chapman & Hall/CRC Press, ISBN 978-1-4200-8266-1, https://books.google.com/books?id=omxatN4lVCkC

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